Diffusion-weighted Magnetic Resonance Imaging Data Research Articles

Abstract 2738: Image-driven biophysical model to predict pathologic response to neoadjuvant therapy in breast cancer

Abstract Introduction: In previous work, we developed an image-driven, patient-specific biophysical model of breast tumor response to neoadjuvant therapy (NAT). In this work, we use this model to characterize response during the course of NAT to predict pathological complete response (pCR). pCR is an important binary pathological outcome metric as patients who achieve pCR experience improved survival outcomes. We hypothesize that with optimized imaging time points within the I-SPY 2 trial study design, we will be able to more accurately characterize dynamic response to therapy and predict eventual pCR. Methods: Our retrospective study included 84 patients (26 pCR, 58 non-pCR) from the publicly-available ACRIN-6698 study.4-7 This substudy to the ongoing I-SPY 2 trial evaluates the effectiveness of diffusion weighted magnetic resonance imaging (DW-MRI) for assessing breast cancer response to NAT. We use serial DW-MRI data to compute the apparent diffusion coefficient (ADC), which is used to approximate tumor cell density distribution between baseline and midtreatment (12 weeks). Cellularity data is fit to our model of tumor growth/treatment response1-3 to obtain biophysical estimates of response, including the tumor cell proliferation rate (k). Estimated spatial proliferation maps of the tumor throughout NAT between imaging time points are used to create a histogram of tumor cell proliferation for each patient and histogram metrics are extracted as features to predict pCR. Receiver operating characteristic (ROC) curves were generated and the area under the curve (AUC), sensitivity (sens), and specificity (spec) were calculated to evaluate prediction performance. Results: Parameterizations of tumor biophysical properties were calculated between baseline and midtreatment. In univariate analysis with model-based proliferative area we achieved a ROC AUC of 0.72 (85% sens, 55% spec). Conventional metrics of percent change in tumor area and mean ADC achieved ROC AUC of 0.52 (50% sens and 69% spec; 23% sens and 97% spec). We constructed multivariate logistic regression models to combine parameters to build signatures for predicting pCR. We achieved AUC of 0.88 (85% sens and 81% spec) and 0.97 (100% sens and 88% spec) for our model metrics alone, and in combination with size and ADC metrics, respectively. Conclusions: Use of image-driven mathematical modeling with the ACRIN-6698 subset of the I-SPY 2 trial enables accurate prediction of tumor response during NAT based on baseline and mid-treatment imaging data. This initial study provides promise for improved prediction accuracy with biophysical modeling. These results suggest that a biophysical modeling method has the potential to aid in response characterization. In future work, we will apply our model to the remainder of the ACRIN-6698 subset to more rigorously assess prediction of pCR and investigate incorporation of tumor perfusion and diffusion data. Citation Format: Haley J. Bowers, Jared A. Weis, Alexandra Thomas, Emily Douglas, Katherine Ansley. Image-driven biophysical model to predict pathologic response to neoadjuvant therapy in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2738.

Evaluating reproducibility and subject-specificity of microstructure-informed connectivity

Tractography enables identifying and evaluating the healthy and diseased brain's white matter pathways from diffusion-weighted magnetic resonance imaging data. As previous evaluation studies have reported significant false-positive estimation biases, recent microstructure-informed tractography algorithms have been introduced to improve the trade-off between specificity and sensitivity. However, a major limitation for characterizing the performance of these techniques is the lack of ground truth brain data. In this study, we compared the performance of two relevant microstructure-informed tractography methods, SIFT2 and COMMIT, by assessing the subject specificity and reproducibility of their derived white matter pathways. Specifically, twenty healthy young subjects were scanned at eight different time points at two different sites. Subject specificity and reproducibility were evaluated using the whole-brain connectomes and a subset of 29 white matter bundles. Our results indicate that although the raw tractograms are more vulnerable to the presence of false-positive connections, they are highly reproducible, suggesting that the estimation bias is subject-specific. This high reproducibility was preserved when microstructure-informed tractography algorithms were used to filter the raw tractograms. Moreover, the resulting track-density images depicted a more uniform coverage of streamlines throughout the white matter, suggesting that these techniques could increase the biological meaning of the estimated fascicles. Notably, we observed an increased subject specificity by employing connectivity pre-processing techniques to reduce the underlaying noise and the data dimensionality (using principal component analysis), highlighting the importance of these tools for future studies. Finally, no strong bias from the scanner site or time between measurements was found. The largest intraindividual variance originated from the sole repetition of data measurements (inter-run).

The effects of stress across the lifespan on the brain, cognition and mental health: A UK biobank study

Repeated overstimulation of the stress response system, caused by exposure to prolonged highly stressful experiences, is thought to affect brain structure, cognitive ability, and mental health. We tested the effects of highly stressful experiences during childhood and adulthood using data from the UK Biobank, a large-scale national health and biomedical study with over 500,000 participants. To do this, we defined four groups with high or low levels of childhood and/or adulthood stress. We then used T1-and diffusion-weighted MRI data to assess the macrostructure of grey matter and microstructure of white matter within limbic brain regions, commonly associated with the stress response. We also compared executive function and working memory between these groups. Our findings suggest that in females, higher levels of Childhood stress were associated with reduced connectivity within the posterior thalamic radiation and cingulum of the hippocampus. In males however, higher levels of Adulthood stress is associated with similar changes in brain microstructure in the posterior thalamic radiation and cingulum of the hippocampus. High stress in Childhood and Adulthood was associated with decreases in executive function and working memory in both males and females. Stress across the lifespan was also positively associated with the number of diagnosed mental health problems, with a stronger effect in females than in males. Finally, our findings also suggest that cognitive and mental health outcomes due to stress may be mediated by the sex specific stress related changes in brain microstructure. Together our findings demonstrate clear links between stress at distinct phases of the lifespan, changes in measures of brain microstructure, impairments in cognitive abilities and negative mental health outcomes.

Application of Multimodal Fusion Technology in Image Analysis of Pretreatment Examination of Patients with Spinal Injury.

As one of the most common imaging screening techniques for spinal injuries, MRI is of great significance for the pretreatment examination of patients with spinal injuries. With rapid iterative update of imaging technology, imaging techniques such as diffusion weighted magnetic resonance imaging (DWI), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), and magnetic resonance spectroscopy are frequently used in the clinical diagnosis of spinal injuries. Multimodal medical image fusion technology can obtain richer lesion information by combining medical images in multiple modalities. Aiming at the two modalities of DCE-MRI and DWI images under MRI images of spinal injuries, by fusing the image data under the two modalities, more abundant lesion information can be obtained to diagnose spinal injuries. The research content includes the following: (1) A registration study based on DCE-MRI and DWI image data. To improve registration accuracy, a registration method is used, and VGG-16 network structure is selected as the basic registration network structure. An iterative VGG-16 network framework is proposed to realize the registration of DWI and DCE-MRI images. The experimental results show that the iterative VGG-16 network structure is more suitable for the registration of DWI and DCE-MRI image data. (2) Based on the fusion research of DCE-MRI and DWI image data. For the registered DCE-MRI and DWI images, this paper uses a fusion method combining feature level and decision level to classify spine images. The simple classifier decision tree, SVM, and KNN were used to predict the damage diagnosis classification of DCE-MRI and DWI images, respectively. By comparing and analyzing the classification results of the experiments, the performance of multimodal image fusion in the auxiliary diagnosis of spinal injuries was evaluated.

Post-stroke enriched auditory environment induces structural connectome plasticity: secondary analysis from a randomized controlled trial

Post-stroke neuroplasticity and cognitive recovery can be enhanced by multimodal stimulation via environmental enrichment. In this vein, recent studies have shown that enriched sound environment (i.e., listening to music) during the subacute post-stroke stage improves cognitive outcomes compared to standard care. The beneficial effects of post-stroke music listening are further pronounced when listening to music containing singing, which enhances language recovery coupled with structural and functional connectivity changes within the language network. However, outside the language network, virtually nothing is known about the effects of enriched sound environment on the structural connectome of the recovering post-stroke brain. Here, we report secondary outcomes from a single-blind randomized controlled trial (NCT01749709) in patients with ischaemic or haemorrhagic stroke (N = 38) who were randomly assigned to listen to vocal music, instrumental music, or audiobooks during the first 3 post-stroke months. Utilizing the longitudinal diffusion-weighted MRI data of the trial, the present study aimed to determine whether the music listening interventions induce changes on structural white matter connectome compared to the control audiobook intervention. Both vocal and instrumental music groups increased quantitative anisotropy longitudinally in multiple left dorsal and ventral tracts as well as in the corpus callosum, and also in the right hemisphere compared to the audiobook group. Audiobook group did not show increased structural connectivity changes compared to both vocal and instrumental music groups. This study shows that listening to music, either vocal or instrumental promotes wide-spread structural connectivity changes in the post-stroke brain, providing a fertile ground for functional restoration.

Longitudinal Changes in Structural Connectivity in Young People at High Genetic Risk for Bipolar Disorder.

Recent studies of patients with bipolar disorder or at high genetic risk reveal structural dysconnections among key brain networks supporting cognitive and affective processes. Understanding the longitudinal trajectories of these networks across the peak age range of bipolar disorder onset could inform mechanisms of illness onset or resilience. Longitudinal diffusion-weighted MRI and phenotypic data were acquired at baseline and after 2 years in 183 individuals ages 12-30 years in two cohorts: 97 unaffected individuals with a first-degree relative with bipolar disorder (the high-risk group) and 86 individuals with no family history of mental illness (the control group). Whole-brain structural networks were derived using tractography, and longitudinal changes in these networks were studied using network-based statistics and mixed linear models. Both groups showed widespread longitudinal changes, comprising both increases and decreases in structural connectivity, consistent with a shared neurodevelopmental process. On top of these shared changes, high-risk participants showed weakening of connectivity in a network encompassing the left inferior and middle frontal areas, left striatal and thalamic structures, the left fusiform, and right parietal and occipital regions. Connections among these regions strengthened in the control group, whereas they weakened in the high-risk group, shifting toward a cohort with established bipolar disorder. There was marginal evidence for even greater network weakening in those who had their first manic or hypomanic episode before follow-up. Neurodevelopment from adolescence into early adulthood is associated with a substantial reorganization of structural brain networks. Differences in these maturational processes occur in a multisystem network in individuals at high genetic risk of bipolar disorder. This may represent a novel candidate to understand resilience and predict conversion to bipolar disorder.

Illness Chronology, Brain Morphology and Mixed Symptom Presentations in Older Age Bipolar Disorder: Emerging Findings from the Global Aging & Geriatric Experiments in Bipolar Disorder Database (GAGE –BD) Project

Roughly 25% of those with bipolar disorder (BD) are 60 years or older, and this proportion is likely to increase in tandem with changes in global demographics. Aging-related trajectories may also start in mid adulthood and lead to worse health outcomes such as cognitive decline and early mortality. The Global Aging & Geriatric Experiments in Bipolar Disorder Database (GAGE-BD) project, a multi-national research initiative, pools archival research and clinical datasets to provide an evidence platform with potential to advance knowledge on older age BD (OABD). Key goals of investigators collaborating on the GAGE-BD project are to examine correlates of age of onset, understand BD mood symptom presentations, and set the stage for future investigations in aging-related changes in brain structure and functioning. In the first presentation, Dr. Lavin will describe an analysis of clinical correlates of late vs. early-onset BD. The majority of patients with BD have an onset before 40 years old. However, a significant number of individuals have onset of illness after age 40, commonly referred to as late-onset BD. This examination of a large international dataset sheds light on the association between the age of onset of BD symptoms and the distribution of risk factors and course of illness. Findings can yield important insights into the mechanisms involved in the evolution of BD across the life-span. In the second presentation, Dr. Blumberg will present on a new initiative to link neuroimaging findings with clinical data from the GAGE –BD sample. This talk will summarize procedures to harmonize aggregate data and how multi-national teams can collaborate to grow research capacity that is intended to better understand aging-related changes in brain structure and the relationship of the changes to demographic and clinical factors. Preliminary findings comparing older adults with BD to healthy participants on gray matter volume from structural magnetic resonance imaging (MRI) data, and white matter microstructure from diffusion-weighted MRI data, will be presented. In the third presentation, Dr. Eyler will discuss BD symptom severity mixity in OABD. The Diagnostic and Statistical Manual (DSM-5) includes a ‘with mixed features’ specifier that applies to a manic /hypomanic or to a depressive episode. Findings from the GAGE-BD samples suggested mixed features are relatively common in OABD, but do not suggest that mixity is associated with functional status. Future studies need to investigate whether mixed symptoms could contribute to BD neuroprogression in later life. Following the presentations, there will be discussion and ample opportunity for questions regarding emerging issues most relevant to OABD

Associations Between White Matter Microstructures and Cognitive Functioning in 8-Year-Old Children: A Track-Weighted Imaging Study.

Quantitative tractography using diffusion-weighted magnetic resonance imaging data is widely used in characterizing white matter microstructure throughout childhood, but more studies are still needed to investigate comprehensive brain-behavior relationships between tract-specific white matter measures and multiple cognitive functions in children. In this study, we analyzed diffusion-weighted MRI data of 71 healthy 8-year-old children utilizing white matter tract-specific quantitative measures derived from diffusion-weighted MRI tractography based on a novel track-weighted imaging approach. Track density imaging, average path length map and 4 track-weighted diffusion tensor imaging measures including: mean diffusivity, fractional anisotropy, axial diffusivity, and radial diffusivity were computed for 63 white matter tracts. The track-weighted imaging measures were then correlated with a comprehensive set of neuropsychological test scores in different cognitive domains including intelligence, language, memory, academic skills, and executive functions to identify tract-specific brain-behavior relationships. Significant correlations (P < .05, false discovery rate corrected; r = 0.27-0.57) were found in multiple white matter tracts, with a total of 40 correlations identified between various track-weighted imaging measures including average path length map, track-weighted imaging-fractional anisotropy, and neuropsychological test scores and subscales. Specifically, track-weighted imaging measures indicative of better white matter connectivity and/or microstructural development significantly correlated with higher IQ and better language abilities. Our findings demonstrate the ability of track-weighted imaging measures in establishing associations between white matter and cognitive functioning in healthy children and can serve as a reference for normal brain/cognition relationships in young school-age children and further aid in identifying imaging biomarkers predictive of adverse neurodevelopmental outcomes.

819 White Matter Activation Pathways in STN and GPi DBS for Parkinson’s Disease

INTRODUCTION: The subthalamic nucleus (STN) and globus pallidus interna (GPi) are common deep brain stimulation (DBS) targets for Parkinson’s disease (PD). DBS is thought to function by modulating downstream circuits, yet targeting is based on anatomy rather than structural connectivity. While tractography using normative connectomes has been used to study the impact of the hyperdirect pathway, cortical connectivity patterns only account for 20% of the variance in DBS response. Less is known about subcortical and cerebellar connectivity, and patient-specific tractography in DBS for PD. METHODS: Preoperative T1-weighted and diffusion-weighted MRI, postoperative CT, and clinical data were acquired for 49 patients (24 STN, 25 GPi) who underwent DBS for PD. After initial programming, the Lead DBS toolbox was used to model VAT based on the individual subject electrode and stimulation parameters. Whole-brain probabilistic tractography was performed from VAT using FSL (FMRIB's Software Library) to estimate activation pathways. Average activation pathways were generated for each target by weighting each subject’s pathways by unilateral MDS-UPDRS-III response to stimulation at initial programming. Comparison tractography was performed using 85-subject PD normative connectome. RESULTS: Analysis of weighted activation pathways reveals that STN-DBS strongly activates pathways to the caudate nucleus and bilateral cerebellum. In contrast, GPi-DBS activates pathways to the putamen and the ansa lenticularis. Furthermore, normative connectome tractography shows that the subcortical and cerebellar structural connectivity is not as well appreciated. Lastly,cortical pathways show that GPi DBS has increased premotor connectivity while STN DBS has robust motor connectivity, and confining the seeds to the STN and GPi does not diminish cortical connectivity. CONCLUSIONS: Activation pathways from STN and GPi therapeutic contacts have unique subcortical and cerebellar connectivity. White matter connections within the basal ganglia and cerebellum, including ansa lenticularis and regions associated with the DRTT, are connected to the therapeutic contacts, and this connectivity is not as well appreciated using normative connectome data. Activation pathways via the internal capsule alone cannot explain cortical connectivity. Further research to test the utility of defined connections using patient-specific tractography may help better predict DBS response or prospectively inform targeting.

Diffusion tensor imaging correlates of depressive symptoms in Parkinson disease.

Depression is a heterogeneous clinical syndrome prevalent in patients with Parkinson disease (PD) that remains incompletely understood. Further, the differences in biomarkers of depression in PD and in non-PD patients are unclear. The subcallosal cingulate cortex (SCC) and its connections have been implicated in the pathophysiology of major depressive disorder (MDD). Diffusion tensor imaging (DTI) provides a tool to quantify MDD-related structural abnormalities underlying depressive symptoms in PD. Diffusion-weighted magnetic resonance imaging data were collected from 31 patients with PD. Depression symptom severity was measured using the Beck Depression Inventory (BDI-II), and assessed using three subscales: dysphoric mood, loss of interest/pleasure, and somatic symptoms. Probabilistic tractography methods were used to quantify the SCC connectivity to target regions in cortico-limbic-striatal network (ventral striatum, medial prefrontal cortex [mPFC], dorsal anterior cingulate cortex, and uncinate fasciculus), while fractional anisotropy (FA) was calculated in predefined white matter regions of interest. DTI data were correlated with severity of depression across three domains. SCC-mPFC connectivity in the left hemisphere was positively correlated with severity of dysphoric mood (Benjamini-Hochberg adjusted p=.02). Region of interest-based analyses demonstrated a significant and distinct topographic association between FA and dysphoric mood, loss of interest/pleasure, and somatic symptom severity, although these findings did not maintain significance after applying the false discovery rate correction. Abnormal SCC connectivity underlies depressive symptoms in both PD and MDD, suggesting that interventions used for MDD should be explored in treating depressive symptoms in PD, particularly depression dominated by dysphoric mood.

Abstract P1-08-20: In silico analysis of a novel mathematical model integrating in vitro and in vivo imaging data reveals driving mechanisms of breast cancer response to NAT for personalized tumor forecasting

Abstract The early assessment of neoadjuvant therapy (NAT) response in triple-negative breast cancer (TNBC) would enable a treating oncologist to adjust a therapeutic plan of a non-responding patient, and thereby enhance outcomes while preventing unnecessary toxicities. To address this challenge, we propose leveraging personalized, in silico forecasts of tumor response to therapeutic regimens via a mechanistic mathematical model calibrated with patient-specific longitudinal magnetic resonance imaging (MRI) data acquired early during NAT. Here, we focus on identifying the driving mechanisms involved in the model formulation through a global sensitivity analysis. Our model describes the dynamics of TNBC cell density as a combination of mobility, which is formulated as a diffusion process constrained by local tumor-induced mechanical stress, and net proliferation, which is represented with a logistic term. To model TNBC response to NAT drug combinations, we adjust the tumor cell proliferation rate with a recent pharmacodynamic model, MuSyC, which also accounts for synergy of potency and efficacy. Tumor cell density is estimated from diffusion-weighted MRI data, while tissue mechanical properties are defined from segmented contrast-enhanced T1-weighted MRI data. To model the heterogeneous intratumoral delivery of drugs, we use perfusion maps estimated from dynamic contrast-enhanced MRI data. NAT drug pharmacokinetics are approximated with a linear model, which reasonably represents their temporal decay during NAT. Sobol’s method is used for the global sensitivity analysis of the NAT response of two different tumors (one well-perfused and one poorly-perfused) on a 3D, tissue-scale domain. This allows us to assess the total effect (ST) of each model parameter on tumor volume and global cellularity. Here, we focus on two standard NAT regimens: doxorubicin plus cyclophosphamide, and paclitaxel plus carboplatin. The parameter space is constructed by integrating three approaches. First, we use prior patient-specific in silico estimates of tumor cell mobility and proliferation. Second, we experimentally constrain the parameters accounting for potential synergistic drug activity by using time-resolved, high-throughput, automated microscopy assays that capture drug-induced changes in proliferation rates of various TNBC lines (HCC1143, SUM149, MDAMB231, and MDAMB468; perfosfamide was used in lieu of the pro-drug cyclophosphamide). Third, we scale the resulting in vitro parameter ranges to be clinically-relevant through an in silico study with our mechanistic model. Our results show that out of the 15 model parameters considered in the sensitivity analysis only a minority exhibited a driving role (ST &amp;gt; 0.1) in representing the dynamics of TNBC response to NAT, namely: the baseline tumor cell proliferation rate along with the effect and ratio of peak concentration to EC50 of doxorubicin, paclitaxel, and carboplatin. The other parameters have a marginal effect and can thus be fixed to any value within the parameter space. We select these constant parameters such that they contribute to simplifying the model formulation, leading to a surrogate reduced model. We further show that the reduced and the original model produce distributions of tumor volume and global cellularity that are in remarkable agreement both qualitatively (Dice similarity coefficient &amp;gt; 0.90 and &amp;gt; 0.94, respectively) and parameter-wise (concordance correlation coefficient &amp;gt; 0.85 and &amp;gt; 0.89, respectively). Thus, we conclude that our reduced model constitutes a feasible surrogate for future clinical calibration-forecasting studies, thereby facilitating personalized NAT response forecasting with patient-specific imaging datasets acquired in vivo. Citation Format: Guillermo Lorenzo, Angela M. Jarrett, Christian T. Meyer, Darren R. Tyson, Vito Quaranta, Thomas E. Yankeelov. In silico analysis of a novel mathematical model integrating in vitro and in vivo imaging data reveals driving mechanisms of breast cancer response to NAT for personalized tumor forecasting [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-08-20.

The SACT Template: A Human Brain Diffusion Tensor Template for School-age Children.

School-age children are in a specific development stage corresponding to juvenility, when the white matter of the brain experiences ongoing maturation. Diffusion-weighted magnetic resonance imaging (DWI), especially diffusion tensor imaging (DTI), is extensively used to characterize the maturation by assessing white matter properties in vivo. In the analysis of DWI data, spatial normalization is crucial for conducting inter-subject analyses or linking the individual space with the reference space. Using tensor-based registration with an appropriate diffusion tensor template presents high accuracy regarding spatial normalization. However, there is a lack of a standardized diffusion tensor template dedicated to school-age children with ongoing brain development. Here, we established the school-age children diffusion tensor (SACT) template by optimizing tensor reorientation on high-quality DTI data from a large sample of cognitively normal participants aged 6-12 years. With an age-balanced design, the SACT template represented the entire age range well by showing high similarity to the age-specific templates. Compared with the tensor template of adults, the SACT template revealed significantly higher spatial normalization accuracy and inter-subject coherence upon evaluation of subjects in two different datasets of school-age children. A practical application regarding the age associations with the normalized DTI-derived data was conducted to further compare the SACT template and the adult template. Although similar spatial patterns were found, the SACT template showed significant effects on the distributions of the statistical results, which may be related to the performance of spatial normalization. Looking forward, the SACT template could contribute to future studies of white matter development in both healthy and clinical populations. The SACT template is publicly available now ( https://figshare.com/articles/dataset/SACT_template/14071283 ).

VERDICT MRI for radiation treatment response assessment in neuroendocrine tumors.

Noninvasive methods to study changes in tumor microstructure enable early assessment of treatment response and thus facilitate personalized treatment. The aim of this study was to evaluate the diffusion MRI model, Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors (VERDICT), for early response assessment to external radiation treatment and to compare the results with those of more studied sets of parameters derived from diffusion-weighted MRI data. Mice xenografted with human small intestine tumors were treated with external radiation treatment, and diffusion MRI experiments were performed on the day before and up to 2 weeks after treatment. The diffusion models VERDICT, ADC, IVIM, and DKI were fitted to MRI data, and the treatment response of each tumor was calculated based on pretreatment tumor growth and post-treatment tumor volume regression. Linear regression and correlation analysis were used to evaluate each model and their respective parameters for explaining the treatment response. VERDICT analysis showed significant changes from day -1 to day 3 for the intracellular and extracellular volume fraction, as well as the cell radius index (p<0.05; Wilcoxon signed-rank test). The strongest correlation between the diffusion model parameters and the tumor treatment response was seen for the ADC, kurtosis-corrected diffusion coefficient, and intracellular volume fraction on day 3 (τ =0.47, 0.52, and -0.49, respectively, p<0.05; Kendall rank correlation coefficient). Of all the tested models, VERDICT held the strongest explanatory value for the tumor treatment response on day 3 (R2 =0.75, p<0.01; linear regression). In conclusion, VERDICT has potential for early assessment of external radiation treatment and may provide further insights into the underlying biological effects of radiation on tumor tissue. In addition, the results suggest that the time window for assessment of treatment response using dMRI may be narrow.

Neural correlates of sleep quality in children: Sex-specific associations shown by brain diffusion tractography.

Sleep quality is important for healthy growth and development of children. We aimed to identify associations between sleep disturbances in healthy children without clinical diagnosis of sleep disorders and brain white matter (WM) microstructure using an advanced diffusion-weighted magnetic resonance imaging (DW-MRI) based tractography analysis, and to explore whether there are sex differences in these associations. Brain DW-MRI data were collected from sixty-two 8-year-old children (28 boys, 34 girls) whose parents also completed Children's Sleep Habits Questionnaire (CSHQ). Track-weighted imaging (TWI) measures were computed from the DW-MRI data for 37 WM tracts in each subject. Sex-specific partial correlation analyses were performed to evaluate correlations between TWI measures and a set of sleep disturbance scores derived from the CSHQ. Significant correlations (P<.05, FDR-corrected; r: .48-.67) were identified in 13 WM tracts between TWI and sleep disturbance scores. Sexually dimorphic differences in correlations between sleep disturbance scores and WM microstructure measurements were observed. Specifically, in boys, daytime sleepiness positively correlated with track-weighted mean or radial diffusivity in 10 WM tracts (bilateral arcuate fasciculus, left cingulum, right middle longitudinal fasciculus, and three bilateral segments of superior longitudinal fasciculus). In girls, total CSHQ score, night walking, or sleep onset delay negatively correlated with track-weighted fractional anisotropy or axial diffusivity in 4 WM tracts (bilateral inferior longitudinal fasciculus and uncinate fasciculus). The findings suggest that sleep disturbances without clinical diagnosis of sleep disorders are associated with lower WM microstructural integrity in children. Additionally, the associations possess unique patterns in boys and girls.

Generalisation of continuous time random walk to anomalous diffusion MRI models with an age-related evaluation of human corpus callosum

Diffusion MRI measures of the human brain provide key insight into microstructural variations across individuals and into the impact of central nervous system diseases and disorders. One approach to extract information from diffusion signals has been to use biologically relevant analytical models to link millimetre scale diffusion MRI measures with microscale influences. The other approach has been to represent diffusion as an anomalous transport process and infer microstructural information from the different anomalous diffusion equation parameters. In this study, we investigated how parameters of various anomalous diffusion models vary with age in the human brain white matter, particularly focusing on the corpus callosum. We first unified several established anomalous diffusion models (the super-diffusion, sub-diffusion, quasi-diffusion and fractional Bloch-Torrey models) under the continuous time random walk modelling framework. This unification allows a consistent parameter fitting strategy to be applied from which meaningful model parameter comparisons can be made. We then provided a novel way to derive the diffusional kurtosis imaging (DKI) model, which is shown to be a degree two approximation of the sub-diffusion model. This link between the DKI and sub-diffusion models led to a new robust technique for generating maps of kurtosis and diffusivity using the sub-diffusion parameters βSUB and DSUB. Superior tissue contrast is achieved in kurtosis maps based on the sub-diffusion model. 7T diffusion weighted MRI data for 65 healthy participants in the age range 19–78 years was used in this study. Results revealed that anomalous diffusion model parameters α and β have shown consistent positive correlation with age in the corpus callosum, indicating α and β are sensitive to tissue microstructural changes in ageing.

Deciphering the Network Effects of Deep Brain Stimulation in Parkinson's Disease.

IntroductionDeep brain stimulation of the subthalamic nucleus (STN-DBS) is an established therapy for Parkinson's disease (PD). However, a more detailed characterization of the targeted network and its grey matter (GM) terminals that drive the clinical outcome is needed. In this direction, the use of MRI after DBS surgery is now possible due to recent advances in hardware, opening a window for the clarification of the association between the affected tissue, including white matter fiber pathways and modulated GM regions, and the DBS-related clinical outcome. Therefore, we present a computational framework for reconstruction of targeted networks on postoperative MRI.MethodsWe used a combination of preoperative whole-brain T1-weighted (T1w) and diffusion-weighted MRI data for morphometric integrity assessment and postoperative T1w MRI for electrode reconstruction and network reconstruction in 15 idiopathic PD patients. Within this framework, we made use of DBS lead artifact intensity profiles on postoperative MRI to determine DBS locations used as seeds for probabilistic tractography to cortical and subcortical targets within the motor circuitry. Lastly, we evaluated the relationship between brain microstructural characteristics of DBS-targeted brain network terminals and postoperative clinical outcomes.ResultsThe proposed framework showed robust performance for identifying the DBS electrode positions. Connectivity profiles between the primary motor cortex (M1), supplementary motor area (SMA), and DBS locations were strongly associated with the stimulation intensity needed for the optimal clinical outcome. Local diffusion properties of the modulated pathways were related to DBS outcomes. STN-DBS motor symptom improvement was highly associated with cortical thickness in the middle frontal and superior frontal cortices, but not with subcortical volumetry.ConclusionThese data suggest that STN-DBS outcomes largely rely on the modulatory interference from cortical areas, particularly M1 and SMA, to DBS locations.

Correcting B0 inhomogeneity-induced distortions in whole-body diffusion MRI of bone

Diffusion-weighted magnetic resonance imaging (DWI) of the musculoskeletal system has various applications, including visualization of bone tumors. However, DWI acquired with echo-planar imaging is susceptible to distortions due to static magnetic field inhomogeneities. This study aimed to estimate spatial displacements of bone and to examine whether distortion corrected DWI images more accurately reflect underlying anatomy. Whole-body MRI data from 127 prostate cancer patients were analyzed. The reverse polarity gradient (RPG) technique was applied to DWI data to estimate voxel-level distortions and to produce a distortion corrected DWI dataset. First, an anatomic landmark analysis was conducted, in which corresponding vertebral landmarks on DWI and anatomic T2-weighted images were annotated. Changes in distance between DWI- and T2-defined landmarks (i.e., changes in error) after distortion correction were calculated. In secondary analyses, distortion estimates from RPG were used to assess spatial displacements of bone metastases. Lastly, changes in mutual information between DWI and T2-weighted images of bone metastases after distortion correction were calculated. Distortion correction reduced anatomic error of vertebral DWI up to 29 mm. Error reductions were consistent across subjects (Wilcoxon signed-rank p < 10–20). On average (± SD), participants’ largest error reduction was 11.8 mm (± 3.6). Mean (95% CI) displacement of bone lesions was 6.0 mm (95% CI 5.0–7.2); maximum displacement was 17.1 mm. Corrected diffusion images were more similar to structural MRI, as evidenced by consistent increases in mutual information (Wilcoxon signed-rank p < 10–12). These findings support the use of distortion correction techniques to improve localization of bone on DWI.

Linking Multi-Modal MRI to Clinical Measures of Visual Field Loss After Stroke.

Loss of vision across large parts of the visual field is a common and devastating complication of cerebral strokes. In the clinic, this loss is quantified by measuring the sensitivity threshold across the field of vision using static perimetry. These methods rely on the ability of the patient to report the presence of lights in particular locations. While perimetry provides important information about the intactness of the visual field, the approach has some shortcomings. For example, it cannot distinguish where in the visual pathway the key processing deficit is located. In contrast, brain imaging can provide important information about anatomy, connectivity, and function of the visual pathway following stroke. In particular, functional magnetic resonance imaging (fMRI) and analysis of population receptive fields (pRF) can reveal mismatches between clinical perimetry and maps of cortical areas that still respond to visual stimuli after stroke. Here, we demonstrate how information from different brain imaging modalities—visual field maps derived from fMRI, lesion definitions from anatomical scans, and white matter tracts from diffusion weighted MRI data—provides a more complete picture of vision loss. For any given location in the visual field, the combination of anatomical and functional information can help identify whether vision loss is due to absence of gray matter tissue or likely due to white matter disconnection from other cortical areas. We present a combined imaging acquisition and visual stimulus protocol, together with a description of the analysis methodology, and apply it to datasets from four stroke survivors with homonymous field loss (two with hemianopia, two with quadrantanopia). For researchers trying to understand recovery of vision after stroke and clinicians seeking to stratify patients into different treatment pathways, this approach combines multiple, convergent sources of data to characterize the extent of the stroke damage. We show that such an approach gives a more comprehensive measure of residual visual capacity—in two particular respects: which locations in the visual field should be targeted and what kind of visual attributes are most suited for rehabilitation.

White matter diffusivity and its correlations to state measures of psychopathology in male refugees with posttraumatic stress disorder

Post-traumatic stress disorder (PTSD) is a heterogenous condition and the underlying neurobiology is still poorly understood. In this study, we tested the hypothesis that PTSD is associated with microstructural changes in white matter (WM) fibre tracts that connect regions involved in emotional processing, memory, attention, and language. Furthermore, we examined how different response patterns to individualized trauma-provoking stimuli related to underlying WM microstructure. Sixty-nine trauma-affected male refugees with PTSD (N = 38) or without PTSD (N = 31) underwent clinical assessments and diffusion-weighted magnetic resonance imaging (DWI) of the whole brain at 3 Tesla. Diffusion tensor metrics were computed from DWI data and used to characterize regional white-matter microstructure. An automated tract segmentation method was used to extract diffusion tensor metrics from subject-based reconstructions of tract segments (ROI), including uncinate fasciculus (UF), cingulum bundle (CB), superior longitudinal fasciculus (SLF) in three subdivisions (SLF I - III), and fibre bundles connecting orbito-frontal cortex to striatum (OF-ST). Outside the scanner we obtained measures of immediate (state) arousal, avoidance and dissociation symptoms assessed in response to auditory exposure to a personal traumatic memory. Using mean FA of the middle part of each ROI, mixed ANOVA revealed a significant interaction between group, ROI and hemisphere. Post-hoc comparisons showed that, relative to refugees without PTSD, refugees with PTSD had lower FA in right CB, left SLF-I, bilateral OF-ST and bilateral SLF-II. Mean FA scaled negatively with avoidance in right CB while mean FA in bilateral UF scaled positively with individual scores reflecting dissociation symptoms. The results support a pathophysiological model of PTSD that implicates limbic structures, prefrontal cortex and striatum. The results also emphasize the need to consider PTSD’s multifaceted manifestations when searching for functional-structural relationships.

Mapping the human corticoreticular pathway with multimodal delineation of the gigantocellular reticular nucleus and high-resolution diffusion tractography

The corticoreticular pathway (CRP) is a major motor tract that transmits cortical input to the reticular formation motor nuclei and may be an important mediator of motor recovery after central nervous system damage. However, its cortical origins, trajectory and laterality are incompletely understood in humans. This study aimed to map the human CRP and generate an average CRP template in standard MRI space. Following recently established guidelines, we manually delineated the primary reticular formation motor nucleus (gigantocellular reticular nucleus [GRN]) using several group-mean MRI contrasts from the Human Connectome Project (HCP). CRP tractography was then performed with HCP diffusion-weighted MRI data (N = 1065) by selecting diffusion streamlines that reached both the cortex and GRN. Corticospinal tract (CST) tractography was also performed for comparison. Results suggest that the human CRP has widespread origins, which overlap with the CST across most of the motor cortex and include additional exclusive inputs from the medial and anterior prefrontal cortices. The estimated CRP projected through the anterior and posterior limbs of the internal capsule before partially decussating in the midbrain tegmentum and converging bilaterally on the pontomedullary reticular formation. Thus, the CRP trajectory appears to partially overlap the CST, while being more distributed and anteromedial to the CST in the cerebrum before moving posterior to the CST in the brainstem. These findings have important implications for neurophysiologic testing, cortical stimulation and movement recovery after brain lesions. We expect that our GRN and tract maps will also facilitate future CRP research.